The exploding volumes of mobile video traffic call for deploying content caches inside mobile operator networks. With in-network caching, users' requests for popular content can be served from a content cache deployed at mobile gateways in vicinity to the end user. This inherently reduces the load on the content servers and the backbone of operator's network. In light of the increasing trend in virtualization of network functions, we propose a cost-effective caching as a service (CaaS) framework for virtual video caching in 5G mobile networks. In order to evaluate the pros and cons of our CaaS approach, we formulate two virtual caching problems, namely maximum return on investment(MRI) and maximum offloaded traffic (MOT). MRI aims at maximizing return on caching investment by finding the best trade-off between the cost of cache storage and bandwidth savings from caching video contents in the mobile network operator (MNO)'s cloud. Likewise, MOT aims to maximize the traffic offloaded from the MNO's core and backhaul within given budget constraints. More specifically, taking the popularity and size of video contents into account, MRI and MOT aim to find the optimal caching tables which maximize the ratio of transmission bandwidth cost to storage cost and the offloaded traffic for a given budget, respectively. We reduce the complexity of the proposed problem formulated as a binary-integer programming (BIP) by using canonical duality theory (CDT). Experimental results obtained using the invasive weed optimization (IWO) have shown significant performance enhancement of the proposed system in terms of return on investment, quality, offloaded traffic, and storage efficiency.

The exploding volumes of mobile video traffic call for deploying content caches inside mobile operators network. With in-network caching, users' requests for popular content can be served from a content cache deployed at mobile gateways in vicinity to the end user, therefore considerably reducing the load on the content servers and the backbone of operator's network. In practice, content caches can be installed at multiple levels inside an operator's network (e.g., serving gateway, packet data network gateway, RAN, etc.), leading to an idea of hierarchical in-network video caching. In order to evaluate the pros and cons of hierarchical caching, in this paper we formulate a cache provisioning problem which aims to find the best tradeoff between the cost of cache storage and bandwidth savings from hierarchical caching. More specifically, we aim to find the optimal size of video caches at different layers of a hierarchical in-network caching architecture which minimizes the ratio of transmission bandwidth cost to storage cost. We overcome the complexity of our problem which is formulated as a binary-integer programming (BIP) by using canonical duality theory (CDT). Numerical results obtained using the invasive weed optimization (IWO) show that important gains can be achieved, with benefit-cost ratio and cost efficiency improvements of more than 43% and 38%, respectively.

In this paper we investigate power-efficient resource allocation for transmission of perceptual quality guaranteed video over LTE downlink under delay quality of service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the downlink under user perceived quality and statistical delay QoS provisioning. We solve the problem using dual decomposition and employ the ellipsoid method to update dual variables. Experimental results have shown significant performance enhancement of the proposed system in terms of power efficiency while satisfying the statistical delay-bounded QoS and perceptual quality requirements.

Reducing the queue length and queuing delay in the buffer which in turn reduces congestion and latency in the network are of great importance for real-time video services. In this paper, we propose a perceptual quality-aware queue management mechanism to shorten the queue length and waiting time of video traffic in a finite buffer. Our proposed approach prioritizes video layers in a stream in accordance with their hierarchical structure and selectively drops packets from different layers under a target user perceived quality constraint. This in turn reduces network load and latency and increases the capacity to serve more concurrent streams while satisfying users' perceptual quality requirements. We develop a statistical queuing model to express the delay requirements of video layers with an equivalent cross-layer constraint We then formulate the queue management problem as a minimization of queuing delay using a selective packet drop approach under perceptual quality and queue expiry time provisioning. Numerical results show that our proposed approach outperforms the widely deployed random early detection (RED) and tail drop (ID) queue management schemes in terms of reducing queuing delay and queue length in the buffer, while increasing the fraction of perceived quality-satisfied users.